Colorectal Cancer Outcomes in a Large Negative Computed Tomography Colonography Screening Cohort
Bibliographic record
Abstract
INTRODUCTION: At our institution, the most common cohort of individuals having computed tomography colonography (CTC) are those that require primary screening for colorectal cancer and were unable to tolerate or failed optical colonoscopy (OC). CTC is an efficient method for detecting polyps, masses, flat-lesions, and overt colorectal cancer, serving as a viable alternative to colonoscopy. This study follows patients with negative CTC results to evaluate the number of clinically significant lesions that may have been potentially missed by CTC. We suspect this number will be exceedingly low given the high sensitivity of this technique. METHODS: All patients with negative CTC screening (n = 509) in the Eastern Health Medical Health Region, located in Newfoundland and Labrador, Canada were included. An electronic medical record review was undertaken, encompassing provider, colonoscopy, imaging, and histopathology reports. Subjects were also checked through the Newfoundland Cancer Clinic Registry Database. All incidents of colorectal cancer were recorded. RESULTS: The study cohort comprised 509 subjects. These subjects were followed for an average of 7.88 years. Two colorectal adenocarcinomas in this cohort were identified representing a crude cancer incidence rate of 0.49 cancers per 1000 patient years, and a rate of 0.39% following a normal CTC. CONCLUSIONS: Colorectal cancer presenting clinically is rare in the 7.88 years following a negative CTC, suggesting CTC is equally effective for colorectal screening compared to OC. Furthermore, current guidelines that recommend interval CTC screening every 5 years is conservative, and interval screening can likely be recommended over a longer time frame.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".